smart grid broeer_thesis_0.pdf

7/26/2019 smart grid Broeer_thesis_0.pdf

1/126

Analysis of Smart Grid and Demand Response Technologies for Renewable Energy

Integration: Operational and Environmental Challenges

by

Torsten Broeer

B.Sc., Portsmouth Polytechnic, 1985

M.Sc., Carl von Ossietzky University of Oldenburg, 2004

A Dissertation Submitted in Partial Fulfillment of the

Requirements for the Degree of

DOCTOR OF PHILOSOPHY

in the Department of Mechanical Engineering

cTorsten Broeer, 2015

University of Victoria

All rights reserved. This dissertation may not be reproduced in whole or in part, by

photocopying or other means, without the permission of the author.


2/126

ii

Analysis of Smart Grid and Demand Response Technologies for Renewable Energy

Integration: Operational and Environmental Challenges

by

Torsten Broeer

B.Sc., Portsmouth Polytechnic, 1985

M.Sc., Carl von Ossietzky University of Oldenburg, 2004

Supervisory Committee

Dr. Ned Djilali, Supervisor

(Department of Mechanical Engineering)

Dr. Andrew Rowe, Departmental Member(Department of Mechanical Engineering)

Dr. Peter Wild, Departmental Member


Dr. G. Cornelis van Kooten, Outside Member

(Department of Economics)


3/126

iii

Supervisory Committee

Dr. Ned Djilali, Supervisor


Dr. Andrew Rowe, Departmental Member


Dr. Peter Wild, Departmental Member(Department of Mechanical Engineering)

Dr. G. Cornelis van Kooten, Outside Member

(Department of Economics)

ABSTRACT

Electricity generation from wind power and other renewable energy sources is in-

creasing, and their variability introduces new challenges to the existing power system,

which cannot cope effectively with highly variable and distributed energy resources.

The emergence of smart grid technologies in recent year has seen a paradigm shift

in redefining the electrical system of the future, in which controlled response of the

demand side is used to balance fluctuations and intermittencies from the generation

side. This thesis investigates the impact of smart grid technologies on the integra-

tion of wind power into the power system. A smart grid power system model isdeveloped and validated by comparison with a real-life smart grid experiment: the

Olympic Peninsula Demonstration Experiment. The smart grid system model is then

expanded to include 1000 houses and a generic generation mix of nuclear, hydro,

coal, gas and oil based generators. The effect of super-imposing varying levels of

wind penetration are then investigated in conjunction with a market model whereby

suppliers and demanders bid into a Real-Time Pricing (RTP) electricity market. The


4/126

iv

results demonstrate and quantify the effectiveness of DR in mitigating the variability

of renewable generation. It is also found that the degree to which Greenhouse Gas

(GHG) emissions can be mitigated is highly dependent on the generation mix. A dis-

placement of natural gas based generation during peak demand can for instance lead

to an increase in GHG emissions. Of practical significance to power system operators,

the simulations also demonstrate that Demand Response (DR) can reduce generator

cycling and improve generator efficiency, thus potentially lowering GHG emissions

while also reducing wear and tear on generating equipment.


5/126

v

Contents

Supervisory Committee ii

Abstract iii

Table of Contents v

List of Tables viii

List of Figures ix

Acknowledgements xiii

Dedication xv

1 Introduction 1

1.1 Motivation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.2.1 The need for more renewable energy . . . . . . . . . . . . . . 5

1.2.2 Renewable energy integration . . . . . . . . . . . . . . . . . . 6

1.2.3 The smart grid and demand response . . . . . . . . . . . . . . 9

1.2.4 Power system modeling. . . . . . . . . . . . . . . . . . . . . . 12

1.2.5 Summary of literature review . . . . . . . . . . . . . . . . . . 13

1.3 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

1.4 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151.5 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2 Modeling and validation 17

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.2 Model system description and grid modeling . . . . . . . . . . . . . . 17

2.2.1 End-use load modeling . . . . . . . . . . . . . . . . . . . . . . 18


6/126

vi

2.2.2 Market. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.3 Case study: The Olympic Peninsula Experiment . . . . . . . . . . . . 21

2.3.1 System modeling . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.4 Simulation, validation and case studies . . . . . . . . . . . . . . . . . 26

2.4.1 Base reference data validation . . . . . . . . . . . . . . . . . . 27

2.4.2 Operational validation . . . . . . . . . . . . . . . . . . . . . . 27

2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3 Wind balancing 33

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

3.2 Electricity market behavior and proposed bidding mechanisms . . . . 33

3.3 Wind power integration . . . . . . . . . . . . . . . . . . . . . . . . . 35

3.3.1 Introducing wind power to The Olympic Peninsula Project . . 35

3.3.2 Scaled up model . . . . . . . . . . . . . . . . . . . . . . . . . 36

3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

4 Mitigation of greenhouse gas emissions 42

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

4.2 System model and simulation approach . . . . . . . . . . . . . . . . . 44

4.2.1 Demand and load modeling . . . . . . . . . . . . . . . . . . . 45

4.2.2 Supply side modeling . . . . . . . . . . . . . . . . . . . . . . . 50

4.2.3 Greenhouse gas emission tracking . . . . . . . . . . . . . . . . 53

4.2.4 Grid modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . 55

4.3 Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

4.3.1 Base case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

4.3.2 Base case and wind power . . . . . . . . . . . . . . . . . . . . 60

4.3.3 Base case and demand response . . . . . . . . . . . . . . . . . 60

4.3.4 Base case, wind power and demand response . . . . . . . . . . 61

4.4 Comparison of emissions . . . . . . . . . . . . . . . . . . . . . . . . . 63

4.4.1 Accumulated emissions . . . . . . . . . . . . . . . . . . . . . . 634.4.2 Individual emissions for fossil fuel based generators . . . . . . 64

4.4.3 Emissions over time. . . . . . . . . . . . . . . . . . . . . . . . 66

4.5 Generator cycling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

4.5.1 Base case with and without wind power . . . . . . . . . . . . 68

4.5.2 Adding demand response. . . . . . . . . . . . . . . . . . . . . 70


7/126

vii

4.6 The limits of demand response and the Battery state of charge . . 72

4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

5 Further Discussion and Conclusions 745.1 Summary of work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

5.2 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

5.3 Perspective and future research . . . . . . . . . . . . . . . . . . . . . 76

A Additional figures to Chapter 4 78

B Technical implementation 80

B.1 Further information. . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

B.2 Programming overview . . . . . . . . . . . . . . . . . . . . . . . . . . 81

Bibliography 108


8/126

viii

List of Tables

Table 4.1 Assumed operations and maintenance cost, startup cost, early

shutdown cost and minimum runtime per generator (power plant) 50

Table 4.2 Typical fossil generation unit heat rates:

(source: [3]) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

Table 4.3 Fossil fuel emissions for coal, gas and oil:(pounds per billion BTU of energy input). . . . . . . . . . . . . 55


9/126

ix

List of Figures

Figure 1.1 Power system overview . . . . . . . . . . . . . . . . . . . . . . 1

Figure 1.2 Balancing supply and demand . . . . . . . . . . . . . . . . . . 2

Figure 1.3 Energy deficit . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Figure 1.4 Aspects of an electrical power system . . . . . . . . . . . . . 4

Figure 1.5 The need for flexibility . . . . . . . . . . . . . . . . . . . . . . 6Figure 1.6 Methodology and input data for modeling wind power impacts 8

Figure 1.7 Load control strategies; adapted from [22] . . . . . . . . . . . 10

Figure 2.1 Average energy consumption for a single family house in the

U.S.A (data source:[48]) . . . . . . . . . . . . . . . . . . . . . 19

Figure 2.2 Residential house model: electrical appliances with varying po-

tential for demand response are shown, along with other vari-

ables such as weather and human behavior. . . . . . . . . . . 20

Figure 2.3 Bidding behavior of the controller of a thermostatic heatingload set between 17 C and 22 C . . . . . . . . . . . . . . . . 21

Figure 2.4 Overview of The Olympic Peninsula Smart Grid Demonstration

Project, where different suppliers and demanders are part of a

double auction real-time electricity market . . . . . . . . . . . 22

Figure 2.5 Variation in the Mid-Columbian wholesale electricity price over

a four day period during December 2006 . . . . . . . . . . . . 24

Figure 2.6 Validation approach: Comparison of base reference data and

operational results from the demonstration project with the

simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Figure 2.7 Comparison of simulation results with the demonstration project:

Average power demand of all houses in the control group over

a weekend 24 hour period. . . . . . . . . . . . . . . . . . . . . 28


10/126

x


Average power consumption of all houses in the RTP group over

a weekday 24 hour period . . . . . . . . . . . . . . . . . . . . 29

Figure 2.9 Market interactions . . . . . . . . . . . . . . . . . . . . . . . . 30


Total load of all houses and commercial buildings over the week

of the experiment . . . . . . . . . . . . . . . . . . . . . . . . . 31

Figure 3.1 Smart grid system model. . . . . . . . . . . . . . . . . . . . . 34

Figure 3.2 The principle of a double auction real-time (RTP) electricity

market:

(a) Market event N: suppliers (wind and hydro) and demanders

bid into the market and determine the market clearing price

(b) Market event N+1: a decline in wind power leads to a higher

market clearing price and the loads automatically switch off . 38

Figure 3.3 Simulated wind power data for the week of the experiment . . 39

Figure 3.4 Simulation results of superimposing wind power on the vali-

dated model, showing two different scenarios:

(a) High wind power and low demand

(b) Low wind power and high demand . . . . . . . . . . . . . 40

Figure 3.5 The behavior of a single house over a 24 hour period to varyingwind power:

(a) Indoor house temperature following wind power

(b) Varying wind power leads to a varying market clearing price

and the switch off of loads . . . . . . . . . . . . . . . . . . . . 41

Figure 4.1 System model . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Figure 4.2 Comparison of the load behavior of the heating system in two

distinctly different residential houses:

(a) Good insulation(b) Poor insulation . . . . . . . . . . . . . . . . . . . . . . . . 46

Figure 4.3 Distribution of heating setpoints for all 1,000 modeled residen-

tial houses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Figure 4.4 Aggregated demand curve of 1,000 typical residential homes in

the Pacific Northwest during a winter season . . . . . . . . . . 48


11/126

xi

Figure 4.5 Diversity of controller ranges (Tmax-Tmin) of 1,000 individual

houses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Figure 4.6 Available capacity of a global generation mix . . . . . . . . . 50

Figure 4.7 Various suppliers are bidding into the market with loads as

price takers (unresponsive demand) . . . . . . . . . . . . . . 51

Figure 4.8 Different suppliers and demanders are part of a double auction

real-time electricity market . . . . . . . . . . . . . . . . . . . 52

Figure 4.9 Wind power during the first week of January . . . . . . . . . 53

Figure 4.10 Methodology for GHG emission tracking, taking into consider-

ation the capacity factors and efficiency for all individual gen-

erators and fuel types. . . . . . . . . . . . . . . . . . . . . . . 54

Figure 4.11 Modified IEEE4 feeder with 1,000 residential houses and fivegenerators, unresponsive loads, all bidding into a double auc-

tion electricity market . . . . . . . . . . . . . . . . . . . . . . 56

Figure 4.12 Various suppliers and the aggregated demand of all 1,000 houses

are bidding into the market, where the demanders are price takers 57

Figure 4.13 Real power vs cleared market quantity . . . . . . . . . . . . . 58

Figure 4.14 Error between cleared market quantity and actual load per mar-

ket interval . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

Figure 4.15 Accumulated power and market clearing quantity over time (en-

ergy) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59Figure 4.16 Market interaction with wind power and the aggregated load

of all individual houses bidding into the market during a high

wind power regime . . . . . . . . . . . . . . . . . . . . . . . . 60

Figure 4.17 Market interaction with a generation mix without wind power

and all individual residential houses bidding into the market . 61

Figure 4.18 Market interaction with demand response and wind power . . 62

Figure 4.19 Comparison of load curves with and without demand response 62

Figure 4.20 Comparison of accumulated emissions. . . . . . . . . . . . . . 64

Figure 4.21 Base case: Emissions per fossil fuel based generator . . . . . . 65

Figure 4.22 Base case and wind power: Emissions per fossil fuel based gen-

erator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

Figure 4.23 Base case and demand response: Emissions per fossil fuel based

generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66


12/126

xii

Figure 4.24 Base case, wind power and demand response: Emissions per

fossil fuel based generator . . . . . . . . . . . . . . . . . . . . 66

Figure 4.25 Comparison of emissions . . . . . . . . . . . . . . . . . . . . . 67

Figure 4.26 Accumulated generator cycling over a period of 1 week:

(a) Base case without wind power.

(b) Base case with wind power. . . . . . . . . . . . . . . . . . 69

Figure 4.27 Accumulated generator cycling over a period of 1 week:

(a) Base case with demand response

(b) Base case with demand response and wind power . . . . . 71

Figure 4.28 The battery state of charge . . . . . . . . . . . . . . . . . . . 72

Figure A.1 Loadcurve of 1,000 residential houses without demand response,

compared to the loadcurve with wind power and demand response 78

Figure A.2 Base case and demand response with wind power: comparison

of energy use of all 1,000 residential houses . . . . . . . . . . . 79

Figure B.1 Overview of programs, input- and output files . . . . . . . . . 81


13/126

xiii

ACKNOWLEDGEMENTS

First and utmost, I would like to express my special appreciation to my supervisor, Dr.

Ned Djilali. Thank you for providing me with so much dedicated support; personally,academically and financially. In particular I would like to thank you for encouraging

my research throughout the different directions it has taken. I would also like to

thank my committee members, Dr. Andrew Rowe, Dr. Peter Wild and Dr. Cornelis

van Kooten for serving as my committee members. Your varied areas of expertise

and discussions have stimulated me in many ways. A special thanks for the support

I have received from IESVIC members: Dr. Jay Sui for always being available and

sharing his passion for photography, Dr. Lawrence Pitt for sharing ideas about life,

wind power, sailing and beer, Dr. Te-Chun Wu (TC) for sharing his home made

beer and the ups and downs of PhD life, Dr. Nigel David for walks and talks at

the marina, Susan Walton for providing so much more than administration expertise,

Peggy White and Barry Kent for all your support. It has always been fun to visit

the IESVIC office. Thank you to my various office and hallway mates. Susan Burton

for her cheerfulness and for teaching me new and important English words such as

bobby pin and discombobulated. Dr. Xun Zhu for her humour and showing me

how to properly prepare green tea. Mike Fischer and Dr. Trevor Williams for all the

discussions we had about our shared concerns for the planet we live on.

Thank you to those at BC Hydro who made data and knowledge available to me:Dr. Magdalena Rucker, Jai Mumick and Dr. Ziad Shawwash.

Thank you to Professor Sonnenschein, Carl von Ossietzky University of Oldenburg

for inviting me to spend time with his research group and enhancing my knowledge

of Smart Grid and Environmental Modelling.

Thank you to Michael Golba who gave me a place to stay in Oldenburg and many

good discussions accompanied by wine and grappa.

Thank you to Professor Geza Joos, McGill for the great talks, support and en-

couragement to bite the bullet when the goings were slow.

I would also like to thank the NSERC Strategic Wind Energy Network (WES-

NET) for their financial support, which enabled my internship at the Pacific North-

west National Laboratory (PNNL). The internship was pivotal for my research and

increased my knowledge of smart grid technologies, modelling and new approaches

for integrating renewable energy into the electrical grid.

Thank you PNNL for providing me with the opportunity to have an internship


14/126

xiv

within the Energy Technology Development Group. You provided me with a labora-

tory to work in and a fantastic group of colleagues. It was a great experience and I

am very grateful for the friendly and open support that I received. I am especially

indebted to Dave Chassin, Jason Fuller and Dr. Frank Tuffner.

Thank you to the Pacific Institute for Climate Solutions (PICS) for your financial

support.

A special thank you to Dr. John Emes, my friend and main proof reader. Your

flexible schedule and willingness to be available are much appreciated. It must have

been hard trying to convince me that some of words I used did not exist in the English

language. Thank you google for showing us that many of those words did exist.

Thank you to my parents Erna and Richard Broer and my sister Silvia Fischer-

Broer and brother in-law Jens Fischer who have always been supportive of what I amdoing.

Thanks to the anonymous Mexican dog in Baha California, who by running in

front of my bicycle caused an accident that lead me to meet my wife Cathy Rzeplinski.

Meeting you is the best thing that has happened in my life! Thank you for all your

love and support.


15/126

xv

DEDICATION

Dank seggen much ik min Ollern

Erna un Richard Broer

dat se immer for mi dor ween sund!


16/126

Chapter 1

Introduction

1.1 Motivation

Sustainability, climate change, increasing cost of fossil fuels and a political imperative

for energy independence have combined to increase interest in the use of renewable

energy sources to meet growing electricity demands, as well partially displacing ex-

isting thermal power generation. Current power systems are still dominated by fossil

fuel based electricity generation and operated on supply following the changing de-

mand. In such systems nuclear and coal plants usually operate as base load power

plants, while other types of power plants, such as hydro and natural gas, balance

the variability on the demand side. The increasing use of renewable energy resources

adds additional complexity to power systems and makes them more challenging to

operate, as illustrated in Figure1.1.

Figure 1.1: Power system overview


17/126

2

Renewable power generation resources can be divided into two groups:

Those which have similar characteristics to conventional power generation fa-

cilities in that they are predictable and controllable. This group includes hy-droelectric generation and the use of biomass.

Those which are variable and intermittent, such as wind and solar.

This research will focus on Variable Renewable Energy sources, and in particular

on the large scale integration of wind power into the electricity system. The dis-

placement of fossil fuels by Variable Renewable Energy Sources (VRES) is considered

to be a viable option for mitigating greenhouse gas emissions. However, compared

with conventional power-generating facilities, VRES have challenging operating char-acteristics such as lower and more variable capacity factors and variable, intermittent

availability. Figure1.2illustrates the extreme case of supply comprised of 100% wind

power and the challenge of balancing a fixed and unresponsive demand with a variable

supply.

Figure 1.2: Balancing supply and demand


18/126

3

Superimposing demand and supply shows periods of both energy deficit and energy

surplus (Figure1.3). The traditional approach to making up for the energy deficit

would be supply-side management by providing reserve capacity from other energy

sources. However, the additional cost and infrastructure required could offset the

economic and environmental benefits of utilizing wind power.

Figure 1.3: Energy deficit

The increasing penetration of wind power and other variable and distributed en-

ergy resources calls for an integrated system approach that includes not only supply

side management, but also the active participation of the demand side in conjunc-

tion with emerging smart grid technologies. This thesis investigates a new approach

to balancing electricity demand and supply by modifying the power consumptionof residential loads in addition to the conventional way of balancing power by load

following.


19/126

4

1.2 Literature review

Optimal operation of the electrical power system is a central objective in power sys-

tems engineering and includes the transmission and distribution grid, generation facil-ities and loads, and interconnections to other power system control areas. The overall

goals are to reduce costs, improve overall system efficiency and ensure system relia-

bility. Traditionally, these operational goals have been achieved mainly by managing

the supply side (SSM) and by trading electricity, when available, with neighbouring

power systems.

A simplified representation of an electrical power system is shown in Figure 1.4.

It includes thermal, hydro electricity generation and VRES on the supply side, which

have to match industrial, commercial and residential consumption on the demand

side at all times.

Hydro

Thermal

Variable

Renewables

Residential

Commercial

Industrial

The Grid

Constraints

Economics

Supply Demand

Resources

Figure 1.4: Aspects of an electrical power system

Todays power system is already complex and poses many challenges for system

operators to ensure grid stability and reliability. The increasing integration of VRES,

such as wind and solar power, adds further complexity and operational difficulties to

the overall system.

This literature review covers the relevant work done to address these issues and

concentrates specifically on the following aspects:

1. The need for more renewable energy


20/126

5

2. Renewable energy integration with an emphasis on wind power

3. Smart grid and demand response

4. Power system modeling

1.2.1 The need for more renewable energy

Climate change underpins much of the motivation for renewable energy. Mounting

scientific evidence has led to the following observations and predictions:

Although numerous and diverse factors contribute to climate change a major

driver of global warming is the increase in atmospheric CO2 and other green

house gases emitted by burning fossil fuels.

Since the beginning of the industrial revolution the world temperature has in-

creased by 0.8 C and the resulting melting of glaciers and polar ice caps has

already led to a rise in sea level of 20 cm.

CO2 levels continue to rise and, without intervention, the temperature of the

planet will rapidly reach what is considered to be the critical limit of 2 C

above pre-industrial level, beyond which major ecosystems are predicted to

begin collapsing.

The International Panel on Climate Change (IPCC) predicts that a business

as usual policy will risk a rise in global temperature of more than 5 C by the

end of the century, with devastating consequences for the worlds economy.

A world-wide effort is necessary to reduce GHG emissions and prevent a looming

climate catastrophe.

The introduction and expansion of renewable energy resources to replace fossil

fuels, coupled with energy conservation initiatives, are the main pillars of a long-termstrategy to achieve the required mitigation of GHG emissions necessary to minimize

global warming.


21/126

6

1.2.2 Renewable energy integration

The balancing challenges introduced by variable renewable resources are addressed

in a report from the IEA (International Energy Agency)[30], which also discuses

pathways for managing power systems with large shares of variable renewables.

The variability and uncertainties of VRES increases the need for a flexible power

system as shown in Figure1.5.

Electric

Power

Systemvariable

renewables

Contingencies

Dispatchable

power plants

Energy storage

Interconnection

with other

markets

Demand Side

mangementDemand

Net load

Fluctations

Needs for flexibility Flexible resources

Figure 1.5: The need for flexibility

Wind power integration

According to the wind energy roadmap from the IEA [31] the worldwide installed

wind energy capacity is expected to grow from 464 GW in 2013 to 1403 GW in 2030.

According to the same source wind power generation cost range from $60/MWh to

$130/MWh and can already be competitive.

However, development of wind power plants requires land with sufficient wind re-

sources. Proximity to the power grid is an asset, but often wind generation sites are

remote from existing transmission lines and load centres. Public opposition due to


22/126

7

visual impact and noise, regulatory requirements and other environmental concerns

are additional factors to be considered. Although wind energy fed into the power

system has the potential to reduce reliance on traditional energy resources and re-

duce emissions, it may necessitate complementary power generation to balance the

inevitable fluctuations in generating capacity. The additional infrastructure could

offset the intended environmental and economic benefits. The optimal placement of

wind turbines is thus influenced by a combination of socio-political, environmental,

technical and economic factors.

An overview of integration of wind power into the power system as well as current

approaches for assessing the technical and economic impacts of large scale wind power

integration are investigated in[1]. Also included are the different methodologies used

and definitions of common terms.

Wind integration studies

Several relevant studies were analyzed by the IEA Wind R&D Task 25; these were

compiled in the final report [27] published in July 2009. A summary paper emphasized

the difficulty in comparing the results from the various studies. Factors such as the

different assessment methodologies, time scales, input data and the different usage of

common terms can lead to misleading interpretation of the results. Wind integration

costs can vary widely and depend upon control area characteristics such as size,

generation portfolio mix, the level of interconnections, the geographic dispersion of

wind resources, level of wind penetration, system reliability and reserve requirements.

Methodology for modeling wind power impacts

Modeling plays an important role in wind integration studies and both the parameters

selected and methods used influence the results. The various modeling approaches are

discussed and categorized in[42] to facilitate an understanding of different approaches.


23/126

8

A summary of modeling approaches impacting wind integration studies is pre-

sented in Figure.1.6. This illustrates that different methods and assumptions lead to

different results and conclusions. The ideal overall simulation method should include

all the different cases (items) and input data. Ideally the factors listed in the shaded

areas should be combined within a single model. However, due to current computa-

tional power limitations this is impractical so approximations and assumptions have

to be made.

Figure 1.6: Methodology and input data for modeling wind power impacts


24/126

9

1.2.3 The smart grid and demand response

Todays electricity system has often been described as the greatest and most complex

machine ever built [21]. While this system is complex, it is not smart. It is still a

highly mechanical system of transmission towers, transmission and distribution lines,

circuit breakers and transformers, the components of which were designed in some

cases a 100 years ago. There is limited use of sensing, monitoring, communication

and control devices throughout the overall system. In recent years a redesigned

power system, often referred to as a Smart Grid, has been proposed. It addresses

the increasing challenges to the power system and offers potential solutions.

Smart grid is a term used to cover a broad spectrum of subjects; some are outside

of the scope of this thesis, but are briefly noted with a few references below.

Communication [23]

Sensing and measurement[25]

Standardization[34]

Regulatory issues [47]

Cyber security [46]

The pathways to a smarter grid are outlined in [9,21,13,8]and include discus-

sion and status assessment of information and communication technology as well as

sensors, monitoring, and control. It is assumed that smart grid technology will trans-

form a centralized, passive power system into one that is dynamic, interactive, and

increasingly customer-centric [18]. Some smart grids concepts have already been

implemented and tested in several projects, such as the Olympic Peninsula Smart

Grid Demonstration Project[26,6].

The benefits of a prospective smart grid have been investigated in several publi-

cations [39,44,15] and and include technical, economical and environmental perfor-mance improvements in comparison to the traditional power system.


25/126

10

History and definitions

The idea of influencing the electricity demand of customers is not new. DSM measures

had already been discussed during the energy crisis at the end 1970s and shortly af-

terwards some of them were implemented [23]. Commercial and industrial customers

were the main targets, and incentives were provided to reduce and change their elec-

tricity consumption when required.

The term DSM first appeared in the literature in the early 1980s. It referred to

different strategies for managing loads rather than supply. A overview of various load

control strategies is presented in [22]and were divided into load shape changes and

load level changes as shown in Figure1.7. Even at this early stage the vision included

flexible load shape that later evolved into the smart grid concept.

Load shape changes Load level changes

Figure 1.7: Load control strategies; adapted from [22]


26/126

11

Demand response

According to the U.S. Department of Energy (DOE), demand response (DR) is defined

as:

Changes in electric usage by end-use customers from their normal con-

sumption patterns in response to changes in the price of electricity over

time, or to incentive payments designed to induce lower electricity use at

times of high wholesale market prices or when system reliability is jeop-

ardized.

Changing the electricity usage of consumers can be translated into three imple-

mentation strategies:

1. Consumers are on call to reduce their usage when the grid is stressed. This

requires predefined contracts between consumers and the utility company, the

ability of Direct Load Control (DLC) and, preferably, knowledge about the

state of the load. An important issue regarding DLC is that of consumers

acceptance, as they may lose control of their energy usage [14].

2. Consumers have the option to react to certain tariff structures such as Time of

Use (TOU). This may require both smart metering and installation of appliances

controllers on the consumer side, in order to make this strategy a reliable DR

resource.

3. Consumers have the ability to react to electricity prices within a Real-Time-

Pricing (RTP) electricity market. This also would require enabling technologies,

such as appliance controllers.

The question How to Get More Response from Demand Response? has been

addressed in [38]. This paper identifies enabling technology that utilizes fast, reliable,

automated communication, that is critical for the effective implementation of DR. It isalso argues that having competitive markets with DR would have significant economic

and political ramifications.

Electricity markets and the different pricing mechanisms are also discussed in

[14]. The author promotes Demand Side Integration (DSI) for integrating flexibility

and controllability into power system operations. Incentive- and price-based demand

response strategies[2] are discussed, where either customers respond directly to price


27/126

12

signals (a market led approach), or a system operator (aggregator or agent) sends

signals to the demand side customers. The author indicates that small consumers are

ready and willing to participate in active demand, especially as the practice of TOU

pricing component is already in place and accepted. However, the level of automation

is important for both user comfort and demand response benefits.

1.2.4 Power system modeling

The requirements for modeling and analyzing energy systems are manifold and may

include factors such as technical, economical, environmental and social aspects. This

section reviews the current approaches to power system modeling and the transitions

required to model both a smarter grid and demand response.

A comparative study of 13 of the most widely used PC based interactive software

packages in the field of power engineering that are used for industrial applications,

education and research was conducted in [29]. The author defines four criteria, which

he believes are essential for the software packages to be effective education/research

tools. These criteria are:

Allow network modeling through per unit representation

Provide the behavior of networks under steady-state and transient conditions

Allow for control of the network for economy/security conditions

Have similarities with energy management systems used in control centres

Additional important criteria include factors such as an open architecture, ex-

pendability via a built-in toolbox and an interface to other systems and libraries.

It has been found that most of the software systems (e.g. PowerWorld) were strong

in analyzing and optimizing AC power flow, but were not capable of dealing with

renewable energy systems in a detailed manner.

Agent based modeling

Requirements for a more intelligent power system design necessarily demand new

electricity system models that go beyond the traditional approaches used for power

system modeling.


28/126

13

A number of Agent Based Models (ABM) have been proposed as a better way

to investigate electrical power systems in terms of power market interaction, grid

congestion and environmental issues and are discussed in [41, 16, 7, 49,35]. ABM,

such as GridLAB-D or Electricity Market Complex Adaptive System (EMACS), rep-

resents the power system with multiple and diverse participants (agents). Each of

the individual agents follow their own objectives, bidding strategies and may have

the ability to learn from past experiences and adapt their behavior.

Load modeling

It has always been of value to predict demand in order to schedule generation facilities

and operate the electrical power system. Load modeling has usually been based

on aggregated metered data from residences, commercial buildings and industrialconsumers [4, 32]. This data-based modeling approach led to a relatively precise

prediction of aggregated demand such as that of the electricity usage of residential

houses.

However, with the introduction of the smart grid concept more detailed load mod-

eling approaches had to be developed. Modeling now had to incorporate and vary the

behavior of individual appliances (e.g. thermostatic loads) and include appropriate

control strategies to achieve the desired demand response outcomes [50].

1.2.5 Summary of literature review

This literature review provides a synoptic overview of the state of the art:

The challenges and approaches of integrating large scale variable renewable

energy sources into the electricity system.

An overview about smart grid and demand response

Power System modelling approaches and load modelling

The review identified the following open questions: What approaches are suitable

for modeling and simulating a smarter grid in order to facilitate further investigation

and understanding of the operation and interaction of individual loads, generators,

markets and controllers within an overall system context? This thesis will especially

focus on modelling and validating of such a system, and on the requirements and

implementation of proper market operations including load and generator bidding.


29/126

14

Furthermore a new methodology for emission tracking and a procedure that accounts

for generator cycling will be introduced.

1.3 Objectives

The overall objective of this research is to determine whether residential loads within

a smart grid architecture can support the integration of wind power. While various

types of residential loads can potentially mitigate the negative impacts of the vari-

ability of wind power, this research focuses on using only one type, space heating, as

a demand response resource. The more specific objectives are to:

Create and validate a smart grid model

Superimpose wind power on the model and show qualitatively, how demand

responds to power surpluses and deficits

Quantify the impact of a smart grid on the potential reduction of green house

gas emissions

Quantify how demand response influences generator cycling when wind power

or other variable generation contributes to the electricity generation system

1.4 Methodology

This thesis proposes a new approach to balancing demand and supply by managing

residential loads instead of the traditional method of adding generating capacity to

match demand. A smart grid power system model was designed and then validated

using actual performance and temporal data from a physical experiment: the Olympic

Peninsula Demonstration Project. Wind power generation was then superimposed

on the validated model. The model incorporates suppliers and demanders who bid

into a real-time pricing (RTP) electricity market. The methodology focuses on the

utilization of selected residential end-use appliances with an intrinsic storage capacity

(thermal loads), that are able to alter their power consumption with minimal effect

on the comfort of the consumers. Loads become responsive and reduce or increase

their consumption depending on both their power needs and current electricity prices.

A surplus of power will result in a lower market price and appliances will respond by


30/126

15

switching on or staying on. Deficits of power will result in a higher market price and

as a consequence appliances will switch off or stay off. Within this double auction

electricity market, these responsive loads behave as additional grid resources.

1.5 Contributions

This research contributes to the development of smart grid system modeling method-

ologies that allow the investigation and analysis of large scale wind energy integration

into the electricity system.

We make fourclaims that are validated in my dissertation:

This work on smart grid modeling and demand response explores and

quantifies pathways to mitigate the problems associated with wind

power integration and includes the following outcomes, whose practical

applicability are demonstrated through validated simulations:

1. Creation and validation of a smart grid model.

2. Identification of the benefits and challenges of demand response.

3. Quantification of the mitigation of GHG emissions.

4. Quantification of the mitigation of generator cycling.


31/126

16

Chapter 2

Modeling and validation

2.1 Introduction

Modeling the electrical power system presents many challenges because it involves the

representation of several subsystems and their interactions, including the generation

side, the demand side, electricity markets, and the transmission and distribution

system. In addition there are many constraints to take care of, such as voltage and

frequency limits and line capacities. With the transformation of the current electricity

system into a smarter grid this modeling task becomes even more complex, especially

as loads now become an active part of the overall power system, and hence a detailed

knowledge about their behavior is also required. The questions are:

1. How do loads behave?

2. How can their behavior be altered?

3. Do the loads exhibit the desired behavior?

This chapter will describe the overall system modeling approach adopted in this

thesis and its validation.

2.2 Model system description and grid modeling

An agent-based modeling environment was utilized for modeling a smart grid power

system using the open source GridLAB-DTM simulation platform[10]. This general

modeling framework includes a range of models and sub-models, accounting for loads ,


32/126

17

market, distribution and transmission system, end-use and their coupled interactions

within the overall system. The variety of component models within GridLAB-DTM

and the array of user determined parameters and variables allows comprehensive

modeling and simulation of a variety of complex electric power systems and scenarios,

and makes this platform particularly well suited to exploring the integration of new

energy technologies. Additionally no open literature studies [33, 17, 37, 36] include

detailed load modeling within an overall system context and therefore GridLAB-

DTM was selected for this study. The application of the model to solve the power

flow problem within a 3-phase unbalanced system utilizes the Three-Phase Current

Injection Method (TCIM) [20]for specific transmission and distribution scenarios.

This section focuses primarily on two general aspects of the system model that

have been further developed as part of this thesis: market modeling and generatormodeling. The system and component models were developed within the GridLAB-

DTM modeling environment. MATLAB was utilized for pre- and postprocessing of

data and for generating some of the GridLAB-DTM macro codes.

2.2.1 End-use load modeling

The electric end-use loads of any house can be divided into two major classes: non-

thermostatic loads, have been such as lights and outlets, and thermostatic loads,

such as Heating, Ventilation, and Air-Conditioning (HVAC) units, water heaters andrefrigerators. Thermostatically controlled loads include some form of intrinsic storage,

such as the thermal mass of the home or water in the tank. Therefore the loads service

function will be maintained during power interruptions over a limited amount of time,

without affecting user comfort.

HVAC systems and water heaters generally have a high potential for demand

response, which depends on factors such as size of system and house, insulation,

location, weather and the recent demand response history. Fig. 2.1shows the average

energy consumption for a single family residential house in the U.S., where space

heating, air conditioning and water heating together account for 66% of the total

energy consumption. Other household appliances, such as lights, have limited or no

demand response potential as switching off these appliances would generally be not

acceptable to customer and adversely effect their comfort.

The house model in Fig.2.2is based on the Equivalent Thermal Parameter (ETP)

model. The ETP model determines the state and power consumption of the HVAC


33/126

18

Figure 2.1: Average energy consumption for a single family house in the U.S.A (datasource:[48])

system while also considering the heat gain through the use of other residential ap-

pliances, and heat gain/loss to the outside environment as a function of weather.

Other household loads were integrated into this model using physical, probabilistic,

and time-varying power consumption models. These models are all available withinthe GridLAB-D development environment [45,5].

2.2.2 Market

Fig.2.3shows the bidding behavior of the controller of HVAC loads during heating

mode. Every load controller observes the electricity market, and automatically places

a bid for power that is influenced by the average market price and standard deviation,

the market clearing price and the current state of the load, defined by the difference

between the current and desired temperature. The bidding price formulation of thecontroller is given in Equation2.1.

Pbid= Pavg +(TcurrentTdesired)khigh/low act

Tmax /minTdesired

(2.1)


34/126

19

Figure 2.2: Residential house model: electrical appliances with varying potential fordemand response are shown, along with other variables such as weather and humanbehavior.

where Pbid is the bid price below which the load will turn on, Paverage is themean price of electricity for the last 24-hour period, Tcurrent is the current indoor

temperature, Tdesired is the desired indoor temperature, khigh/low are the predefined

comfort setting, act is standard deviation of the electricity price for the last 24-hour

period, Tmax/min is the maximum or minimum temperature range.

In this example, the upper and lower setpoints for the desired room temperature

are 22 C and 17 C and the intelligent controller of the heating appliances places

price and power bids into the market according to its power needs. A high room

temperature results in a lower price bid, and no bid at all when the room temperature

is 22 C or higher. A lower temperature results in a higher price bid with a maximum

possible market price (cap price) when the temperature falls below the 17 C threshold

set by residents as their minimum comfort level. A bid at the cap price ensures that

the bid is always successful in purchasing power. Under this condition the load now

behaves as an unresponsive load, as it is only bidding the fixed cap price into the

market and purchases power at whatever the market clearing price might be.


35/126

20

Figure 2.3: Bidding behavior of the controller of a thermostatic heating load setbetween 17 C and 22 C

The setpoints of the controllers are determined by the individual consumer and

therefore the heating system of each house reacts differently depending on the con-

sumers desires for comfort versus money.

2.3 Case study: The Olympic Peninsula Experi-

ment

The Olympic Peninsula Demonstration Project was conducted between April 2006

and March 2007 for the U.S. Department of Energy (DOE) and the Pacific North-

west GridWiseTM

Testbed under the leadership of the Pacific Northwest NationalLaboratory (PNNL). The project was undertaken to investigate how electricity pric-

ing could be used to manage congestion on an experimental feeder. A Real-Time

Pricing (RTP) electricity market with an interval of 5 minutes was established to

facilitate more active participation of end-use appliances and distributed generation

within the electricity system. A dynamic pricing mechanism was implemented, where

suppliers and demanders offered bids into a common market. A simplified represen-


36/126

21

tation of the overall demonstration project is shown in Fig. 2.4.

Figure 2.4: Overview of The Olympic Peninsula Smart Grid Demonstration Project,where different suppliers and demanders are part of a double auction real-time elec-tricity market

One part of the demand side was comprised of a commercial building, backed

up by two diesel generators of 175 kW and 600 kW. The building load represented a

resource capacity and was able to place price and power bids into the market. Under

certain market and bidding conditions, the building could effectively disconnect itself

from the grid by transferring power generation to the diesel units.

Another part of the demand side resource consisted of 112 residential houses

retrofitted with intelligent appliances capable of receiving and responding to price

signals from the electricity market. This enabled a home to automatically change

power consumption based on the current market price of electricity. The aggregate

load when all the responsive devices are on is approximately 75 kW. Each partici-


37/126

22

pating house operated on one of three different types of electricity contracts: fixed,

Time-of-Use (TOU) with critical peak price (CPP), and RTP. For comparison, an

experimental control group of standard non-participating houses was included.

In addition to the commercial building and the residential houses, the project also

included two municipal water-pumping stations. These two pumping stations offered

about 150 kW of controllable load into the market.

The project demonstrated that, for a single experimental feeder, peak loads and

distribution congestion could be reduced by enabling loads to interact within a market

clearing process. More information about the Olympic Peninsula smart grid exper-

iment can be found in [26, 6] and is presented in the system model that duplicates

this experiment.

2.3.1 System modeling

This section presents a smart grid power system model replicating the supply, de-

mand, distribution, transmission and market of the Olympic Peninsula Demonstra-

tion Project.

Transmission and distribution

The entire transmission system is modeled as a single slack bus feeding into the dis-

tribution system. The distribution grid model is based on the physical characteristicsof the Olympic Peninsula Experiment (OPE).

This model presents an unconstrained transmission grid above the connection

point of the feeder, capable of providing infinite power. However, the electricity

market limits the supply so that the feeder capacity is effectively constrained to

maximum capacity of 750 kW. This constraint represents a transmission line capacity

limit of one of the supply lines to the Olympic Penninsula system.

Supply

The supply is represented by two entities. The first is bulk electricity from the Mid-

Columbian wholesale market. For the physical model of the power system, this supply

appears to have infinite capacity. However, the actual supply is controlled by market

dynamics, where the power quantity supply bid from the Mid-Columbian (MID-C)

market is always 750 kW at a wholesale price based on the MID-C electricity mix as

shown in Fig.2.5. This effectively constrains the feeder capacity to a limit of 750 kW.


38/126

23

The second supply entity is a micro-turbine that provides an additional distributed

supply of 30 kW.

Figure 2.5: Variation in the Mid-Columbian wholesale electricity price over a fourday period during December 2006

Demand

The demand side in the OPE incorporates a variety of residential houses and a com-

mercial building with back up generation. Appropriate and detailed load and house

models are required to represent realistic system behavior. The following subsections

describe the residential house model and a model of the backup generator for the

commercial building.

One-hundred and twelve (112) individual residential houses are modeled using

data extracted from the OPE. The data includes the size, type and thermal prop-

erties of houses, used appliances and occupancy mode. The weather, settings of the

appliances and human behavior all have salient influences on the power system and

are included within the system model Fig. 2.2. Different schedules and thermostat

settings are used to reflect the various occupancy patterns, home heating and hot

water usage that together represent the major responsive loads.


39/126

24

Generator model

The commercial load with two back-up diesel generators is a unique feature to the

Olympic Peninsula demonstration. If the market clearing price exceeds the building

bid, the generators will turn on and effectively remove the building from the feeder

system.

New generator controllers were developed to allow generators bidding into a whole-

sale or retail market. The generators bidding behaviors are characterized by the gen-

erator cost curve and include fixed cost, fuel costs, start up and shut down costs. The

building bid is determined by the cost of producing power from its backup genera-

tors that represent a potential negative load. Since the generators are diesel-fueled,

yearly runtime allowances are a key component of the bid price formulation. Equation

(2.2) includes the various parameters contributing to the bid price.

bid price= license premium(fuel cost...

+O&M cost+startup cost . . .

+shutdown penalty) (2.2)

where:license premium: factor used to weight the

bid price by the number of

remaining licensed operation

hours remaining in the year

fuel cost: fuel cost for running 1 hour

O&M cost: operating and maintenance costs

per capacity-time

startup cost: projected penalties associated

with starting the unit

shutdown penalty: projected penalties associatedwith a premature shutdown

of the unit.

The basis and detail formulation of this equation are given in [26]. In particular,

the license premium term includes the influence of yearly runtime restrictions and

how many hours have been used by the plant to date. For example, if the generator


40/126

25

runs a significant portion of its hour limit early in the year, the remaining hours are

ascribed a higher value since they need to last the rest of the year.

In the OPE, both generators were attached to the same building, allowing dif-

ferent portions of the building load to be switched from one generator to the other,

as appropriate. However, to simplify modeling and simulation, two buildings were

assumed, each with one generator attached to it.

2.4 Simulation, validation and case studies

This section explains the simulation and validation approach. It involved refining

the model by calibrating the input data, and validating the simulation results by

comparing them with actual data from the Olympic Peninsula Demonstration Project.

Figure 2.6: Validation approach: Comparison of base reference data and operationalresults from the demonstration project with the simulation

The last week of December 2006 was chosen as a reference period to run the

corresponding simulation. Although the OPE extended over a period of one year; the

simulations were restricted to this week, as it was the only week during the heating

season with consistent and complete data. All reference data utilized are publicly

available within the analysis section of the GridLAB-D website [24].

Given the complexity of the physical system and the intractability of resolving all

the details and temporal scales, an exact reproduction of the field data is unrealistic.


41/126

26

Rather, the objective was to verify that model and demonstration project behavior

exhibit similar characteristics. In order to determine the correctness of the model,

the primary validation approach is based on data and operational representativeness

of the model.

2.4.1 Base reference data validation

Base reference data were extracted from the demonstration project and introduced

into the model in order to create a physically representative environment in which to

conduct the simulation. These data included weather, schedules, thermostat settings

and the characteristics of all 112 individual houses. The setpoints and schedules for

the HVAC and hot water system model reflected the effects of seasonal changes, such

as winter and summer, and usage patterns for weekdays and weekends. Additional

loads were represented as scheduled constant impedance, current and power (ZIP)

loads [40]. These additional loads were divided into two categories: responsive and

unresponsive loads. Unresponsive loads included appliances that would not respond

to the market, such as lights, plug loads, clothes washers, clothes dryers, dishwashers,

cooking ranges, and microwaves. Responsive loads are influenced by the market (like

the HVAC and water heater explicit models) and include refrigerator and freezer

loads.

2.4.2 Operational validation

With the base reference data extracted and helping to define the basic physical aspects

of the system, the behavior of these underlying systems needs to be validated. The

behavior of the various load devices and the electricity market on the system were

both validated to ensure similar behavior to the original OPE.

Load validation

After reproducing the base data of the demonstration project, the behavior of the

aggregated load was tested and validated. First, the load behavior of both the fixed

and control house groups was tested and validated. The load curve of each house is

mainly influenced by weather and thermostat setpoints and schedules, which reflect

human behavior, as illustrated in Fig.2.2.


42/126

27

Fig.2.7shows the average power demand of houses in the control group over a 24-

hour period. The actual behavior of houses in the Olympic Peninsula Demonstration

Project are compared with the corresponding group from the simulations. Both

exhibit similar characteristics with good overall agreement in power levels and ramp

up/down rates, except for some discrepancy around T= 15 hrs. Given the complex

dynamics of the system, it is difficult to ascribe this to a particular component of

the model. Although some of the discrepancy can be attributed to the small sample

of houses and some of the scheduling mismatch, adjustments would at this stage be

somewhat arbitrary.

0:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 24:000

1

2

3

4

5

6

7

8

Power(kW)

Time (hour)

Control weekend

Data

Simulation

Figure 2.7: Comparison of simulation results with the demonstration project: Averagepower demand of all houses in the control group over a weekend 24 hour period.

Second, the load behavior of both the RTP and TOU house groups was tested and

validated. Since the appliances in these houses were retrofitted with intelligent, price

responsive controllers, it had to be shown that the appliances reacted appropriately

to price signals. This involved feeding the market clearing prices from the OPE into

the system model via time series data.

At this stage of the validation process, the loads reacted to the price data from the

project by switching on or off without placing bids into the market. Fig.2.8shows


43/126

28

that the modeled houses with their controlled appliances show similar behavior in

comparison to the real life experiment.

0:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 24:000

1

2

3

4

5

6

7

8

Power(kW)

Time (hour)

RTP weekday

Data

Simulation

Figure 2.8: Comparison of simulation results with the demonstration project: Averagepower consumption of all houses in the RTP group over a weekday 24 hour period

Market validation

In this section, the full market dynamics, including market pricing, were tested and

validated. This involves a double auction RTP market, where the residential loads on

RTP-contracts receive and place bids into the market. In comparison to the previous

load validation process, the intelligent load controllers place their own bids into the

market that depend on the state of the loads and the current market price.

In addition, commercial buildings place bids into the market by offering to switchoff the total building loads. The bid price and quantity depend on the operating costs

of the backup generators to produce electricity, as described in equation ( 2.2).

The market interaction between electricity suppliers and demanders are shown

in Fig. 2.9. It illustrates one specific market event in the system. The market was

updated every 5 minutes. The simulation time-step for buildings and appliances was

set to 15 seconds as it must be significantly smaller than the market cycle time. This


44/126

29

ensures that the fidelity of load diversity is preserved, and prevents the loads from

turning on and off simultaneously when the market cycles.

Figure 2.9: Market interactions

The substation supply is represented by the wholesale price obtained from the Dow

Jones MID-C Electricity Index. This power bid is always 750 kW and is constrained in

order to mimic the feeder limit. The bid price varies according to the price fluctuations

shown in Fig.2.5.A 30 kW micro-turbine is the second seller and bids its maximum capacity with a

varying price into the market. The micro turbine is located downstream of the feeder,

and therefore the total available supply capacity exceeds the feeder limit by 30 kW.

The commercial building always bids its corresponding load into the market at

a price that is equal to the cost of running the backup generators. If the market

clearing price exceeds the bid price, then the backup generators turn on and the

building removes itself from the grid. This is the reason why the generator capacity

appears on the demand side.

On the pure demand side, houses that are on the RTP tariff bid into the market.

Depending on their power needs, the power and price bids vary for each participating

house. The houses which are on TOU tariff do not bid into the market. However,

they react to the changing cost of electricity throughout the day, during times such

as off-peak, mid-peak and on-peak periods. The other houses are part of the fixed

and control groups. None of these houses bid into the market and their loads appear


45/126

30

as unresponsive on the demand curve.

A comparison of the simulated and experimental total load behavior is shown in

Fig.2.10.

Sat Sun Mon Tue Wed Thu Fri Sat0

100

200

300

400

500

600

700

800

Week of 20061223

Power(kW)

Feeder limit

Field data

Simulation

Figure 2.10: Comparison of simulation results with the demonstration project: Totalload of all houses and commercial buildings over the week of the experiment

This includes all the price responsive and non-price responsive sellers and buyers.The salient features are well captured by the simulations, aside from the higher fre-

quency fluctuations which are not resolved by the simulation time steps, and some

discrepancies that are particularly noticeable at the end of the week (Fri.-Sat.). This

is attributed to a systematic offset in solar gains in the model which used weather

data obtained from a location (airport) that was cloudier. The model insolation levels

are thus lower than the average insolation for the geographically distributed houses

in the OPE. Overall, the results indicate that not only is the market behaving appro-

priately, but also provide additional confirmation that individual devices respond to

the market behavior appropriately.

2.5 Summary

In this chapter a modeling and simulation framework is provided,in which an agent-

based model is successfully used to validate a smart grid environment. In the following


46/126

31

chapter further investigation will be conducted to explore the effects of superimposing

wind power on the previously validated model.


47/126

32

Chapter 3

Wind balancing

3.1 Introduction

Balancing demand and supply in power systems currently focuses mainly on the

management of the supply side (supply side management) by controlling the supply

in such a way that supply follows the demand (load following). However, variable

electricity consumption combined with an increased penetration of wind power will

make this an even more challenging task than it already is today. The ability to

selectively switch loads off may be an effective way to offset the variability of wind and

to meet demand during periods of insufficient generation. The potential and impacts

of including responsive loads into the electrical power system with the presence of

wind power will be the main focus of this chapter.

3.2 Electricity market behavior and proposed bid-

ding mechanisms

An overview of a simplified overall smart grid electricity system model is shown in

Fig.3.1. It incorporates an electricity market, end-use models, generator and electricload models. Price signals are used to change the traditional behavior of loads in

order to achieve market based demand response reaction.

The market model represents a double auction RTP electricity market with sell-

ers and buyers bidding into a common market. The basic market interactions are

illustrated in Fig.3.2.

Appliances and other end-use devices in residential homes or commercial buildings


48/126

33

Figure 3.1: Smart grid system model

represent buyers. Appliances, such as HVAC systems and water heaters are equipped

with intelligent controllers [19], which independently and automatically place price

and power demand bids into the market. The electricity suppliers represent the sellers,


49/126

34

who also place price and power bids into the market. The intersection point of the

supply and demand curves sets the market clearing price and quantity of power.

Fig. 3.2a illustrates how all parts of the loads and all suppliers contribute to

setting market prices. Unresponsive loads, such as lights, bid the maximum price

into the market in order to guarantee that they remain in operation. Although the

bid price of the unresponsive loads is always fixed at the maximum bid price, the

changing bid quantity will result in a shift of the demand curve and thus influence

the clearing price. Responsive loads vary their bid prices according to their internal

states and power needs. Generators that place bids below the market clearing price

are guaranteed to sell power at that clearing price. Consumers who are on RTP and

TOU contracts may respond to the changing market prices and curtail their demand

when prices are high. Customers who are on fixed contract do not react to marketprices and, along with other unresponsive loads, they form the unresponsive part in

the demand curve.

Fig.3.2b illustrates a new market event, in which the supply of wind power to the

overall power mix is reduced. This results in a new and higher market clearing price.

As a consequence, some buyers, whose bids were previously successful, are now below

the higher clearing price and consequently have to shut off. This example illustrates

how demand response operates, and how the desired demand behavior to changing

wind power is achieved.

3.3 Wind power integration

This section examines the impacts of demand response on wind power integration.

First wind power is added to the previously validated model of the OPE. With the

expected simulation behavior of the OPE being maintained the model was then scaled

up to a larger model by introducing 35 MW of wind power and increasing the popula-

tion to 10,000 houses. This larger model shares the model framework of the validated

OPE model and provides a larger and diverse basis than the OPE for further study.

3.3.1 Introducing wind power to The Olympic Peninsula Project

Wind power was not part of the Olympic Peninsula Demonstration Project. The

incorporated wind power output data shown in Fig.3.3 were derived from 10-minute

wind data sets measured at the William R. Fairchild International Airport (KCLM),


50/126

35

located within the Olympic Penninsula demonstration area. The wind speed was

converted to the hub height of an Enercon E-33 wind turbine and the power output

calculated using its power curve.

Wind power is an additional supply to the existing power generation mix, con-

necting in a way similar to the micro-turbine. This means the wind power is located

downstream in the simulated feeder, adding to the overall power capacity of the

feeder. It is modeled as a negative load, which bids its corresponding power capacity

and price into the market. Wind power generators have no fuel cost and usually

place low (zero) market bids into the market. This ensures that the bids are below

the market clearing price and this guarantees that the electricity from wind power

will be sold. However, as a consequence, a market situation, such as that shown in

Fig. 3.4 (a)when a high wind power meets low demand, electricity will be sold for$0/MWh.

The strategy of placing bids of $0/MWh works until wind power penetration in-

creases to the point where electricity generation from wind meets or exceeds the

demand so often that a bid and market price of $0/MWh becomes uneconomical.

At this stage a new bidding strategy that includes the real production costs of wind

power generation such as capital cost, maintenance cost and wind integration cost is

required. As electricity demand and supply change with time, different market situa-

tions arise. For example, if electricity generation from wind power drops, generation

from other, higher-priced, power sources will result in a higher market clearing price,such as shown in Fig. 3.4(b). In response, loads with bids that are lower than the

market clearing price will switch off. Thus loads are responsive to decreasing power

generation from wind power.

3.3.2 Scaled up model

The previous modeling methodology is now applied to a RTP-only model with 10,000

residential houses and increased wind power bidding into a double auction electricity

market. The supply side is represented by a 35 MW wind park consistently bidding at

$0/kWh and hydro supply always bidding at $0.1/kWh. Fig. 3.5 shows how a single

residential house responds to varying wind power.

The responsive demand is represented by an HVAC load that bids into the market.

When wind power decreases, the clearing price rises and the load bid falls below

the clearing price. Accordingly, the HVAC system loses the bid and switches off.


51/126

36

Such an event can be observed in Fig. 3.5at around 5:30AM, where a drop in wind

power causes the heating system to switch off for approximately one hour. As a

consequence, the air temperature of the house drops and eventually approaches the

lower temperature limit (17 C for example). The HVAC system now reenters the

market with a bid of the maximum possible market price (the preset cap-price) to

prevent the temperature from dropping below the minimum set value. The formerly

responsive HVAC load is now unresponsive and cannot react to market signals as it

is maintaining the preset minimum temperature. This leads to a high variability of

the bids, however the thermostat automatically protects against fast cycling of the

device.

As wind power increases the clearing price falls and the HVAC system recovers

and its bids remain below the market price cap. However, high wind power regimescan also result in unresponsive load behavior, because wind drives the price down and

HVAC bids are always successful. This will result in indoor house temperatures close

to the upper temperature limit. At this stage, the HVAC system stops purchasing

power and no longer participates in the market.

3.4 Summary

Simulation results show that traditionally passive loads may become a resource that

can mitigate the consequences of winds variability. Various residential loads that are

the preferred candidates for demand response strategies have been identified. Chang-

ing the behavior of these loads depending on wind power deficits or wind power surplus

is a fundamental issue of this research work. The impact of demand response on gen-

erator cycling and the consequences on the mitigation of green house gas emission

will be evaluated in subsequent chapters.


52/126

37

Figure 3.2: The principle of a double auction real-time (RTP) electricity market:(a) Market event N: suppliers (wind and hydro) and demanders bid into the marketand determine the market clearing price(b) Market event N+1: a decline in wind power leads to a higher market clearingprice and the loads automatically switch off


53/126

38

Figure 3.3: Simulated wind power data for the week of the experiment


54/126

39

0 0.2 0.4 0.6 0.8 1 1.20

100

200

300

400

Price$/MWh

Quantity MW

Timestamp 20061224 23:20:00 PSTMarket ID 1432

0 0.2 0.4 0.6 0.8 1 1.20

100

200

300

400

Price$/MWh

Quantity MW

Timestamp 20061228 08:55:00 PSTMarket ID 2411

Figure 3.4: Simulation results of superimposing wind power on the validated model,showing two different scenarios:(a) High wind power and low demand(b) Low wind power and high demand


55/126

40

Figure 3.5: The behavior of a single house over a 24 hour period to varying windpower:(a) Indoor house temperature following wind power(b) Varying wind power leads to a varying market clearing price and the switch offof loads


56/126

41

Chapter 4

Mitigation of greenhouse gas

emissions

4.1 Introduction

Energy use and climate change are closely related. In industrial countries, electricity

consumption can be subdivided into commercial, industrial and residential electric-

ity demand in almost equal parts [12]. Fossil fuel based electricity generation still

has a dominant share of overall electricity generation and is a major factor in the

contribution to GHG emissions.

The replacement of fossil fuels by renewable energy sources is viewed as one of the

most viable options for large scale mitigation of greenhouse gas emissions. However,

our current electricity system was not designed to cope with the large scale integration

of variable, renewable energy resources, such as wind and solar. A more flexible power

system is required [30] that also includes the demand side within an integrated system

approach.

This chapter investigates the energy usage of residential homes and their contribu-

tion to GHG emissions, and explores how both demand response and the additional

use of wind power can mitigate emissions of GHG. These emissions depend on thegeneration mix (primary energy) that is used to generate the electricity.

A detailed smart grid power system model is created, where suppliers and de-

manders are bidding into a double auction electricity market. In this scenario, the

demand is represented by 1,000 residential houses and the supply by a hypothetical

highly fossil fuel-based generation mix. Wind power is superimposed as an additional


57/126

42

supply source. Based on this model the following questions are explored:

What is the demand curve of these houses?

How much GHG will be emitted based on the assumed generation mix?

What happens when DR is introduced to the system and how does this affect

GHG emissions?

What happens when wind power is introduced into the initial system (without

DR) and how does this affect GHG emissions?

How does the combination of both DR and wind power affect GHG emissions?


58/126

43

4.2 System model and simulation approach

The system model contains all the traditional components of an electrical power

system. This includes the transmission system, which is modeled as a single slackbus, and a detailed representation of a distribution system. The supply consists of

different generators such as hydro, coal, nuclear and natural gas. All supply options

have different generation costs and different GHG emissions associated with them.

The demand side consists of 1,000 residential houses with a diversity typical of houses

in the Pacific Northwest. Additionally, a RTP electricity market is introduced where

not only the supply side, but also the demand side places bids into the market.

Figure 4.1: System model

The following sections give further background about the components of the sys-


59/126

44

tem model. This includes the modeling approach for achieving a diversity of loads,

the supply side in the overall system model, and the methodology of GHG emission

tracking .

4.2.1 Demand and load modeling

The demand side consists of 1,000 residential houses with various appliances as shown

in Fig.4.1. This demand is strongly influenced by factors such as weather, thermostat

settings, and other human behavior. Generally, loads can be subdivided into two

types: responsive and unresponsive loads where some loads are more suitable for

demand response than others.

Load diversity

To achieve effective demand response interactions, a diversity of loads is important to

ensure that the household loads do not all react in the same way. This is achieved by

creating model houses, each with different loads, load properties, and load behavior.

Load behavior varies due to factors, such as house size and design, energy efficiency,

occupancy, and load usage.

Fig.4.2illustrates the load behavior of two houses with quite different properties.

The figure shows the operation of the heating system with a conventional thermostat

and the influence of insulation on the heating system power consumption of the twohouses.

The heating system contributes greatly to the overall power consumption of an

individual house. Additionally, the different setpo

smart grid broeer_thesis_0.pdf

Documents